app.py

import gradio as gr

# Load the model and tokenizer
model_name = "mattshumer/Reflection-Llama-3.1-70B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

def generate_response(message, history):
    # Combine history and new message
    prompt = "\n".join([f"Human: {h[0]}\nAI: {h[1]}" for h in history])
    prompt += f"\nHuman: {message}\nAI:"
    
    # Tokenize and generate
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=500, temperature=0.7)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Extract only the AI's response
    ai_response = response.split("AI:")[-1].strip()
    return ai_response

# Create the Gradio interface
iface = gr.ChatInterface(
    fn=generate_response,
    title="Chat with Reflection-Llama-3.1-70B",
    description="Ask me anything!",
)

# Launch the interface
iface.launch()